"""Routine for decoding the CIFAR-10 binary file format."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
# Process images of this size. Note that this differs from the original CIFAR
# image size of 32 x 32. If one alters this number, then the entire model
# architecture will change and any model would need to be retrained.
IMAGE_SIZE = 24
# Global constants describing the CIFAR-10 data set.
NUM_CLASSES = 10
NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000

def read_cifar10(filename_queue):
    """Reads and parses examples from CIFAR10 data files.
    Recommendation: if you want N-way read parallelism, call this function
    N times.  This will give you N independent Readers reading different
    files & positions within those files, which will give better mixing of
    examples.
    Args:
        filename_queue: A queue of strings with the filenames to read from.
    Returns:
        An object representing a single example, with the following fields:
            height: number of rows in the result (32)
            width: number of columns in the result (32)
            depth: number of color channels in the result (3)
            key: a scalar string Tensor describing the filename & record number for this example.
            label: an int32 Tensor with the label in the range 0..9.
            uint8image: a [height, width, depth] uint8 Tensor with the image data
    """
    class CIFAR10Record(object):
        pass
    result = CIFAR10Record()
    # Dimensions of the images in the CIFAR-10 dataset.
    # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the
    # input format.
    label_bytes = 1  # 2 for CIFAR-100
    result.height = 32
    result.width = 32
    result.depth = 3
    image_bytes = result.height * result.width * result.depth
    # Every record consists of a label followed by the image, with a
    # fixed number of bytes for each.
    record_bytes = label_bytes + image_bytes
    # Read a record, getting filenames from the filename_queue.  No
    # header or footer in the CIFAR-10 format, so we leave header_bytes
    # and footer_bytes at their default of 0.
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    result.key, value = reader.read(filename_queue)
    # Convert from a string to a vector of uint8 that is record_bytes long.
    record_bytes = tf.decode_raw(value, tf.uint8)
    # The first bytes represent the label, which we convert from uint8->int32.
    result.label = tf.cast(tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
    # The remaining bytes after the label represent the image, which we reshape
    # from [depth * height * width] to [depth, height, width].
    depth_major = tf.reshape(tf.strided_slice(record_bytes, [label_bytes], [label_bytes + image_bytes]), [result.depth, result.height, result.width])
    # Convert from [depth, height, width] to [height, width, depth].
    result.uint8image = tf.transpose(depth_major, [1, 2, 0])
    return result

def _generate_image_and_label_batch(image, label, min_queue_examples, batch_size, shuffle):
    """Construct a queued batch of images and labels.
    Args:
        image: 3-D Tensor of [height, width, 3] of type.float32.
        label: 1-D Tensor of type.int32
        min_queue_examples: int32, minimum number of samples to retain in the queue that provides of batches of examples.
        batch_size: Number of images per batch.
        shuffle: boolean indicating whether to use a shuffling queue.
    Returns:
        images: Images. 4D tensor of [batch_size, height, width, 3] size.
        labels: Labels. 1D tensor of [batch_size] size.
    """
    # Create a queue that shuffles the examples, and then
    # read 'batch_size' images + labels from the example queue.
    num_preprocess_threads = 16
    if shuffle:
        images, label_batch = tf.train.shuffle_batch( [image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 * batch_size, min_after_dequeue=min_queue_examples)
    else:
        images, label_batch = tf.train.batch( [image, label], batch_size=batch_size, num_threads=num_preprocess_threads, capacity=min_queue_examples + 3 * batch_size)
    # Display the training images in the visualizer.
    tf.summary.image('images', images)
    return images, tf.reshape(label_batch, [batch_size])

def distorted_inputs(data_dir, batch_size):
    """Construct distorted input for CIFAR training using the Reader ops.
    Args:
        data_dir: Path to the CIFAR-10 data directory.
        batch_size: Number of images per batch.
    Returns:
        images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
        labels: Labels. 1D tensor of [batch_size] size.
    """
    filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in range(1, 6)]
    for f in filenames:
        if not tf.gfile.Exists(f):
            raise ValueError('Failed to find file: ' + f)
    # Create a queue that produces the filenames to read.
    filename_queue = tf.train.string_input_producer(filenames)
    # Read examples from files in the filename queue.
    read_input = read_cifar10(filename_queue)
    reshaped_image = tf.cast(read_input.uint8image, tf.float32)
    height = IMAGE_SIZE
    width = IMAGE_SIZE
    # Image processing for training the network. Note the many random
    # distortions applied to the image.
    # Randomly crop a [height, width] section of the image.
    distorted_image = tf.random_crop(reshaped_image, [height, width, 3])
    # Randomly flip the image horizontally.
    distorted_image = tf.image.random_flip_left_right(distorted_image)
    # Because these operations are not commutative, consider randomizing
    # the order their operation.
    distorted_image = tf.image.random_brightness(distorted_image, max_delta=63)
    distorted_image = tf.image.random_contrast(distorted_image, lower=0.2, upper=1.8)
    # Subtract off the mean and divide by the variance of the pixels.
    float_image = tf.image.per_image_standardization(distorted_image)
    # Set the shapes of tensors.
    float_image.set_shape([height, width, 3])
    read_input.label.set_shape([1])
    # Ensure that the random shuffling has good mixing properties.
    min_fraction_of_examples_in_queue = 0.4
    min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * min_fraction_of_examples_in_queue)
    print('Filling queue with %d CIFAR images before starting to train. This will take a few minutes.' % min_queue_examples)
    # Generate a batch of images and labels by building up a queue of examples.
    return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=True)

def inputs(eval_data, data_dir, batch_size):
    """Construct input for CIFAR evaluation using the Reader ops.
    Args:
        eval_data: bool, indicating if one should use the train or eval data set.
        data_dir: Path to the CIFAR-10 data directory.
        batch_size: Number of images per batch.
    Returns:
        images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
        labels: Labels. 1D tensor of [batch_size] size.
    """
    if not eval_data:
        filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in range(1, 6)]
        num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
    else:
        filenames = [os.path.join(data_dir, 'test_batch.bin')]
        num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
    for f in filenames:
        if not tf.gfile.Exists(f):
            raise ValueError('Failed to find file: ' + f)
    # Create a queue that produces the filenames to read.
    filename_queue = tf.train.string_input_producer(filenames)
    # Read examples from files in the filename queue.
    read_input = read_cifar10(filename_queue)
    reshaped_image = tf.cast(read_input.uint8image, tf.float32)
    height = IMAGE_SIZE
    width = IMAGE_SIZE
    # Image processing for evaluation.
    # Crop the central [height, width] of the image.
    resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image, height, width)
    # Subtract off the mean and divide by the variance of the pixels.
    float_image = tf.image.per_image_standardization(resized_image)
    # Set the shapes of tensors.
    float_image.set_shape([height, width, 3])
    read_input.label.set_shape([1])
    # Ensure that the random shuffling has good mixing properties.
    min_fraction_of_examples_in_queue = 0.4
    min_queue_examples = int(num_examples_per_epoch * min_fraction_of_examples_in_queue)
    # Generate a batch of images and labels by building up a queue of examples.
    return _generate_image_and_label_batch(float_image, read_input.label, min_queue_examples, batch_size, shuffle=False)